Robust automatic segmentation of various anatomical structures in a medical image is a key enabler in improving clinical workflows. Here, the term segmentation refers to the identification of the anatomical structure in the medical image by, e.g., delineation of the boundaries of the anatomical structure, by labeling of the voxels enclosed by the boundaries, etc. Once such segmentation has been performed, it is possible to extract clinical parameters such as, in case of, e.g., a cardiac structure, ventricular mass, ejection fraction and wall thickness. Additionally or alternatively, the segmentation may be presented to a user for enabling the user to gather clinical or anatomical information from the segmentation.
It is known to segment an anatomical structure in a medical image using a model. Such type of segmentation is also referred to as model-based segmentation. The model may be defined by model data. The model data may define a geometry of the anatomical structure, e.g., in the form of a multi-compartmental mesh of triangles. Inter-patient and inter-disease-stage shape variability may be modeled by assigning an affine transformation to each part of the model. Affine transformations cover translation, rotation, scaling along different coordinate axes and shearing. Moreover, mesh regularity may be maintained by interpolation of the affine transformations at the transitions between different parts of the model. Such affine transformations are often used as a component in so-termed ‘deformable’ models.
The applying of a model to the image data of the medical image may involve an adaptation technique, also termed ‘mesh adaptation’ in case of a mesh-based model. Such applying is therefore also referred to as ‘adapting’ or ‘fitting’. The adaptation technique may optimize an energy function based on an external energy term which adapts the model to the image data and an internal energy term which maintains a rigidness of the model.
Models of the above described type, as well as other types, are known per se, as are adaptation techniques for the applying of such models to a medical image.
For example, a publication titled “Automatic Model-based Segmentation of the Heart in CT Images” by O. Ecabert et al., IEEE Transactions on Medical Imaging 2008, 27(9), pp. 1189-1201, describes a model-based approach for the automatic segmentation of the heart from three-dimensional (3D) Computed Tomography (CT) images.
It may occur that a medical image shows only part of an anatomical structure of the patient. A reason for this may be that the imaging modality used in acquiring the medical image may only provide a limited field of view of the patient's interior. Other reasons for the medical image showing only part of the anatomical structure may be that other parts may be occluded, may not be visible in the used imaging modality, etc.